{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d8d57924-c3df-47c6-a81f-fd5e5718d2f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score, confusion_matrix\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import itertools\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.linear_model import PassiveAggressiveClassifier\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a61e7107-c4a4-425a-875b-1c9b5ca0025e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>title</th>\n",
       "      <th>text</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8476</td>\n",
       "      <td>You Can Smell Hillary’s Fear</td>\n",
       "      <td>Daniel Greenfield, a Shillman Journalism Fello...</td>\n",
       "      <td>FAKE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10294</td>\n",
       "      <td>Watch The Exact Moment Paul Ryan Committed Pol...</td>\n",
       "      <td>Google Pinterest Digg Linkedin Reddit Stumbleu...</td>\n",
       "      <td>FAKE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3608</td>\n",
       "      <td>Kerry to go to Paris in gesture of sympathy</td>\n",
       "      <td>U.S. Secretary of State John F. Kerry said Mon...</td>\n",
       "      <td>REAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10142</td>\n",
       "      <td>Bernie supporters on Twitter erupt in anger ag...</td>\n",
       "      <td>— Kaydee King (@KaydeeKing) November 9, 2016 T...</td>\n",
       "      <td>FAKE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>875</td>\n",
       "      <td>The Battle of New York: Why This Primary Matters</td>\n",
       "      <td>It's primary day in New York and front-runners...</td>\n",
       "      <td>REAL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0                                              title  \\\n",
       "0        8476                       You Can Smell Hillary’s Fear   \n",
       "1       10294  Watch The Exact Moment Paul Ryan Committed Pol...   \n",
       "2        3608        Kerry to go to Paris in gesture of sympathy   \n",
       "3       10142  Bernie supporters on Twitter erupt in anger ag...   \n",
       "4         875   The Battle of New York: Why This Primary Matters   \n",
       "\n",
       "                                                text label  \n",
       "0  Daniel Greenfield, a Shillman Journalism Fello...  FAKE  \n",
       "1  Google Pinterest Digg Linkedin Reddit Stumbleu...  FAKE  \n",
       "2  U.S. Secretary of State John F. Kerry said Mon...  REAL  \n",
       "3  — Kaydee King (@KaydeeKing) November 9, 2016 T...  FAKE  \n",
       "4  It's primary day in New York and front-runners...  REAL  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Read the data\n",
    "df=pd.read_csv(r'D:\\Anaconda3\\1_news.csv')\n",
    "\n",
    "#Get shape and head\n",
    "df.shape\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3819ed36-2ed9-48e9-81e2-826c939f68ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    FAKE\n",
       "1    FAKE\n",
       "2    REAL\n",
       "3    FAKE\n",
       "4    REAL\n",
       "Name: label, dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#DataFlair - Get the labels\n",
    "labels=df.label\n",
    "labels.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "db35f4f6-c385-4956-bc55-79ad1b8446a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#DataFlair - Split the dataset\n",
    "x_train,x_test,y_train,y_test=train_test_split(df['text'], labels, test_size=0.2, random_state=7)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f83fdc6e-4e91-4023-b7dc-35679cabf260",
   "metadata": {},
   "outputs": [],
   "source": [
    "#DataFlair - Initialize a TfidfVectorizer\n",
    "tfidf_vectorizer=TfidfVectorizer(stop_words='english', max_df=0.7)\n",
    "\n",
    "#DataFlair - Fit and transform train set, transform test set\n",
    "tfidf_train=tfidf_vectorizer.fit_transform(x_train) \n",
    "tfidf_test=tfidf_vectorizer.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d5f2af1e-c306-42d1-8dac-e112f9b70aa6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 92.74%\n"
     ]
    }
   ],
   "source": [
    "#DataFlair - Initialize a PassiveAggressiveClassifier\n",
    "pac=PassiveAggressiveClassifier(max_iter=50)\n",
    "pac.fit(tfidf_train,y_train)\n",
    "\n",
    "#DataFlair - Predict on the test set and calculate accuracy\n",
    "y_pred=pac.predict(tfidf_test)\n",
    "score=accuracy_score(y_test,y_pred)\n",
    "print(f'Accuracy: {round(score*100,2)}%')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3f0f16a1-e6ef-4d25-acaa-d3b895de65a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[587,  51],\n",
       "       [ 41, 588]], dtype=int64)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#DataFlair - Build confusion matrix\n",
    "confusion_matrix(y_test,y_pred, labels=['FAKE','REAL'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5c4be22f-fbb6-4069-833b-f3b1a40f11aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 92.74%\n",
      "[[588  50]\n",
      " [ 42 587]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix\n",
    "from sklearn.model_selection import train_test_split, learning_curve, validation_curve\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.linear_model import PassiveAggressiveClassifier\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# Read the data\n",
    "df = pd.read_csv(r'D:\\Anaconda3\\1_news.csv')\n",
    "\n",
    "# Get shape and head\n",
    "df.shape\n",
    "df.head()\n",
    "\n",
    "# Get the labels\n",
    "labels = df.label\n",
    "labels.head()\n",
    "\n",
    "# Split the dataset\n",
    "x_train, x_test, y_train, y_test = train_test_split(df['text'], labels, test_size=0.2, random_state=7)\n",
    "\n",
    "# Initialize a TfidfVectorizer\n",
    "tfidf_vectorizer = TfidfVectorizer(stop_words='english', max_df=0.7)\n",
    "\n",
    "# Fit and transform train set, transform test set\n",
    "tfidf_train = tfidf_vectorizer.fit_transform(x_train)\n",
    "tfidf_test = tfidf_vectorizer.transform(x_test)\n",
    "\n",
    "\n",
    "# Predict on the test set and calculate accuracy\n",
    "y_pred = pac.predict(tfidf_test)\n",
    "score = accuracy_score(y_test, y_pred)\n",
    "print(f'Accuracy: {round(score*100, 2)}%')\n",
    "\n",
    "# Build confusion matrix\n",
    "cm = confusion_matrix(y_test, y_pred, labels=['FAKE', 'REAL'])\n",
    "print(cm)\n",
    "\n",
    "# 1. Plotting Learning Curve\n",
    "train_sizes, train_scores, test_scores = learning_curve(\n",
    "    pac, tfidf_train, y_train, cv=5, n_jobs=-1, train_sizes=np.linspace(0.1, 1.0, 10), scoring='accuracy'\n",
    ")\n",
    "\n",
    "# Calculate mean and std deviation for train/test scores\n",
    "train_mean = np.mean(train_scores, axis=1)\n",
    "train_std = np.std(train_scores, axis=1)\n",
    "test_mean = np.mean(test_scores, axis=1)\n",
    "test_std = np.std(test_scores, axis=1)\n",
    "\n",
    "# Plot learning curve\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(train_sizes, train_mean, label='Training Accuracy', color='blue')\n",
    "plt.plot(train_sizes, test_mean, label='Cross-validation Accuracy', color='green')\n",
    "plt.fill_between(train_sizes, train_mean - train_std, train_mean + train_std, color='blue', alpha=0.2)\n",
    "plt.fill_between(train_sizes, test_mean - test_std, test_mean + test_std, color='green', alpha=0.2)\n",
    "plt.title('Learning Curve')\n",
    "plt.xlabel('Training Set Size')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# 2. Plotting Validation Curve for 'max_iter'\n",
    "param_range = np.arange(10, 200, 10)\n",
    "train_scores, test_scores = validation_curve(\n",
    "    PassiveAggressiveClassifier(), tfidf_train, y_train, param_name=\"max_iter\", param_range=param_range, cv=5, n_jobs=-1, scoring='accuracy'\n",
    ")\n",
    "\n",
    "# Calculate mean and std deviation for train/test scores\n",
    "train_mean = np.mean(train_scores, axis=1)\n",
    "train_std = np.std(train_scores, axis=1)\n",
    "test_mean = np.mean(test_scores, axis=1)\n",
    "test_std = np.std(test_scores, axis=1)\n",
    "\n",
    "# Plot validation curve\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(param_range, train_mean, label='Training Accuracy', color='blue')\n",
    "plt.plot(param_range, test_mean, label='Cross-validation Accuracy', color='green')\n",
    "plt.fill_between(param_range, train_mean - train_std, train_mean + train_std, color='blue', alpha=0.2)\n",
    "plt.fill_between(param_range, test_mean - test_std, test_mean + test_std, color='green', alpha=0.2)\n",
    "plt.title('Validation Curve for max_iter')\n",
    "plt.xlabel('max_iter')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5b2d3bd5-9738-4003-8e61-8fd5a3a3d8ea",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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